Early diagnosis and treatment of Alzheimer's disease: new definitions and challenges

EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals

BACKGROUND AND OBJECTIVE

Deep learning models have gained widespread adoption in healthcare for accurate diagnosis through the analysis of brain signals. Neurodegenerative disorders like Alzheimer's Disease (AD) and Frontotemporal Dementia (FD) are increasingly prevalent due to age-related brain volume reduction. Despite advances, existing models often lack comprehensive multi-class classification capabilities and are computationally expensive. This study addresses these gaps by proposing EEGConvNeXt, a novel convolutional neural network (CNN) model for detecting AD and FD using electroencephalogram (EEG) signals with high accuracy.

MATERIALS AND METHOD

In this research, we employ an open-access EEG signal public dataset containing three distinct classes: AD, FD, and control subjects. We then constructed a newly proposed EEGConvNeXt model comprised of a 2-dimensional CNN algorithm that firstly converts the EEG signals into power spectrogram-based images. Secondly, these images were used as input for the proposed EEGConvNeXt model for automated classification of AD, FD, and a control outcome. The proposed EEGConvNeXt model is therefore a lightweight model that contributes to a new image classification CNN structure based on the transformer model with four primary stages: a stem, a main model, downsampling, and an output stem.

RESULTS

The EEGConvNeXt model achieved a classification accuracy of ∼95.70% for three-class detection (AD, FD, and control), validated using a hold-out strategy. Binary classification cases, such as AD versus FD and FD versus control, achieved accuracies exceeding 98%, demonstrating the model's robustness across scenarios.

CONCLUSIONS

The proposed EEGConvNeXt model demonstrates high classification performance with a lightweight architecture suitable for deployment in resource-constrained settings. While the study establishes a novel framework for AD and FD detection, limitations include reliance on a relatively small dataset and the need for further validation on diverse populations. Future research should focus on expanding datasets, optimizing architecture, and exploring additional neurological disorders to enhance the model's utility in clinical applications.

Multi-Modal Fusion and Longitudinal Analysis for Alzheimer's Disease Classification Using Deep Learning

Background: Addressing the complex diagnostic challenges of Alzheimer's disease (AD), this study introduces FusionNet, a groundbreaking framework designed to enhance AD classification through the integration of multi-modal and longitudinal imaging data. Methods: FusionNet synthesizes inputs from Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) scans, harnessing advanced machine learning strategies such as generative adversarial networks (GANs) for robust data augmentation, lightweight neural architectures for efficient computation, and deep metric learning for precise feature extraction. The model uniquely combines cross-sectional and temporal data, significantly enhancing diagnostic accuracy and enabling the early detection and ongoing monitoring of AD. The FusionNet architecture incorporates specialized feature extraction pathways for each imaging modality, a fusion layer to integrate diverse data sources effectively, and attention mechanisms to focus on salient diagnostic features. Results: Demonstrating superior performance, FusionNet achieves an accuracy of 94%, with precision and recall rates of 92% and 93%, respectively. Conclusions: These results underscore its potential as a highly reliable diagnostic tool for AD, facilitating early intervention and tailored treatment strategies. FusionNet's innovative approach not only improves diagnostic precision but also offers new insights into the progression of Alzheimer's disease, supporting personalized patient care and advancing our understanding of this debilitating condition.

Prevalence of ApoE Alleles in a Spanish Population of Patients with a Clinical Diagnosis of Alzheimer's Disease: An Observational Case-Control Study

Background and Objectives: Alzheimer's dementia is a progressive neurodegenerative disease that affects memory abilities due to genetic and environmental factors. A well-known gene that influences the risk of Alzheimer's disease is the apolipoprotein E (APOE) gene. The APOE gene is involved in the production of a protein that helps transport cholesterol and other types of fat in the bloodstream. Problems in this process are thought to contribute to the development of Alzheimer's disease. APOE comes in several forms, which are called alleles (ε2, ε3, ε4). Materials and Methods: Therefore, our study aims to identify those subjects with a higher genetic risk through the polymorphism of the APOE gene, using a population screening in patients with a clinical diagnosis of AD in a region of Spain, Castilla y León, as potential biomarkers and to identify individuals at increased genetic risk by polymorphism of the APOE gene. An observational case-control study was conducted in Castilla y León (Spain). Saliva samples were collected and the ApoE gene was analyzed by PCR and agarose gel electrophoresis, respecting ethical criteria. Results: In the Alzheimer's population in Castilla y León, a high prevalence of ApoE3 (74%) was found, followed by ApoE4 (22%); in addition, a higher presence of the ε4 allele was found in the Alzheimer's disease (AD) group than in the control group. It was also observed that the ε2/ε2 genotype was not found in any individual with AD but was found in healthy subjects and that the opposite was observed for the ε4/ε4 genotype. The odds ratio (OR) indicated a risk four times greater of having AD if having the ε4 allele. Conclusions: The demonstrated relation between the different isoforms and the likelihood of developing AD has led to its consideration as a biomarker and a potential pre-symptomatic therapy. The molecular mechanisms that confer a disruptive and protective role to ApoE4 and ApoE2, respectively, are still being studied.

Exploring advancements in early detection of Alzheimer's disease with molecular assays and animal models

Alzheimer's Disease (AD) is a challenging neurodegenerative condition, with overwhelming implications for affected individuals and healthcare systems worldwide. Animal models have played a crucial role in studying AD pathogenesis and testing therapeutic interventions. Remarkably, studies on the genetic factors affecting AD risk, such as APOE and TREM2, have provided valuable insights into disease mechanisms. Early diagnosis has emerged as a crucial factor in effective AD management, as demonstrated by clinical studies emphasizing the benefits of initiating treatment at early stages. Novel diagnostic technologies, including RNA sequencing of microglia, offer promising avenues for early detection and monitoring of AD progression. Therapeutic strategies remain to evolve, with a focus on targeting amyloid beta (Aβ) and tau pathology. Advances in animal models, such as APP-KI mice, and the advancement of anti-Aβ drugs signify progress towards more effective treatments. Therapeutically, the focus has shifted towards intricate approaches targeting multiple pathological pathways simultaneously. Strategies aimed at reducing Aβ plaque accumulation, inhibiting tau hyperphosphorylation, and modulating neuroinflammation are actively being explored, both in preclinical models and clinical trials. While challenges continue in developing validated animal models and translating preclinical findings to clinical success, the continuing efforts in understanding AD at molecular, cellular, and clinical levels offer hope for improved management and eventual prevention of this devastating disease.

Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review

Background and Aims

Alzheimer's disease (AD) is a degenerative neurological condition that worsens over time and leads to deterioration in cognitive abilities, reduced memory, and, eventually, a decrease in overall functioning. Timely and correct identification of Alzheimer's is essential for effective treatment. The systematic study specifically examines the application of deep learning (DL) algorithms in identifying AD using three-dimensional (3D) imaging methods. The main goal is to evaluate these methods' current state, efficiency, and potential enhancements, offering valuable insights into how DL could improve AD's rapid and precise diagnosis.

Methods

We searched different online repositories, such as IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and others, to thoroughly summarize current research on DL methods to diagnose AD by analyzing 3D imaging data published between 2020 and 2024. We use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure the organization and understandability of the information collection process. We thoroughly analyzed the literature to determine the primary techniques used in these investigations and their findings.

Results and Conclusion

The ability of convolutional neural networks (CNNs) and their variations, including 3D CNNs and recurrent neural networks, to detect both temporal and spatial characteristics in volumetric data has led to their widespread use. Methods such as transfer learning, combining multimodal data, and using attention procedures have improved models' precision and reliability. We selected 87 articles for evaluation. Out of these, 31 papers included various concepts, explanations, and elucidations of models and theories, while the other 56 papers primarily concentrated on issues related to practical implementation. This article introduces popular imaging types, 3D imaging for Alzheimer's detection, discusses the benefits and restrictions of the DL-based approach to AD assessment, and gives a view toward future developments resulting from critical evaluation.

Effects of Dance-Based Aerobic Training on Mental Health and Quality of Life in Older Adults with Mild Cognitive Impairment

(1) Background: Mild cognitive impairment in older adults is a condition characterized by a decrease in mental abilities that affects their quality of life. The aim of this study is to evaluate the effects of an aerobic training program based on dance on depression, sleep quality, and quality of life in older adults with mild cognitive impairment. (2) Methods: This study employed a randomized controlled trial design with a total of 92 older adults with cognitive impairment, randomly assigned to an experimental group (n = 47) undergoing dance-based aerobic training and a control group (n = 45) who did not receive any intervention. Depression was assessed using the Yesavage Geriatric Depression Scale, sleep quality through the Pittsburgh Sleep Quality Index (PSQI), and quality of life through the SF-36 questionnaire. (3) Results: Statistically significant improvements were observed in depression (t(46) = 4.783, p = 0.000) and in the PSQI domains: subjective sleep quality (t(46) = 3.333, p = 0.002, and Cohen's d = 0.35), sleep duration (t(46) = 5.511, p = 0.000, and Cohen's d = 0.73) and PSQI total score (t(46) = 2.116, p = 0.040, and Cohen's d = 0.20). Regarding quality of life, improvements were observed in all domains of the questionnaire: the general health (t(46) = -9.374, p = 0.000, and Cohen's d = 0.03), physical function (t(46) = -9.374, p = 0.000, and Cohen's d = 0.03), the physical role (t(46) = -5.954, p = 0.000, and Cohen's d = 1.06), the emotional role (t(46) = -6.200, p = 0.000, and Cohen's d = 0.80), social function (t(46) = -5.585, p = 0.000, and Cohen's d = 0.53), physical pain, (t(46) = -9.224, p = 0.000, and Cohen's d = 1.04), vitality (t(46) = 2.289, p = 0.027, and Cohen's d = 1.27), mental health, (t(46) = -7.985, p = 0.000, and Cohen's d = 1.33), the physical summary component, (t(46) = -13.532, p = 0.000, and Cohen's d = 1.81), and in the mental summary component (t(46) = -10.6 81, p = 0.000, and Cohen's d = 0.06); (4) Conclusions: The results of the present study showed that they suggest that a dance-based aerobic training program improves mental health and quality of life in older people with mild cognitive impairment, providing a non-pharmacological approach to improve general well-being in this population.

Advances in blood biomarkers for Alzheimer disease (AD): A review

Alzheimer disease (AD) and Alzheimer Disease and Related Dementias (AD/ADRD) are growing public health challenges globally affecting millions of older adults, necessitating concerted efforts to advance our understanding and management of these conditions. AD is a progressive neurodegenerative disorder characterized pathologically by amyloid plaques and tau neurofibrillary tangles that are the primary cause of dementia in older individuals. Early and accurate diagnosis of AD dementia is crucial for effective intervention and treatment but has proven challenging to accomplish. Although testing for AD brain pathology with cerebrospinal fluid (CSF) or positron emission tomography (PET) has been available for over 2 decades, most patients never underwent this testing because of inaccessibility, high out-of-pocket costs, perceived risks, and the lack of AD-specific treatments. However, in recent years, rapid progress has been made in developing blood biomarkers for AD/ADRD. Consequently, blood biomarkers have emerged as promising tools for non-invasive and cost-effective diagnosis, prognosis, and monitoring of AD progression. This review presents the evolving landscape of blood biomarkers in AD/ADRD and explores their potential applications in clinical practice for early detection, prognosis, and therapeutic interventions. It covers recent advances in blood biomarkers, including amyloid beta (Aβ) peptides, tau protein, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP). It also discusses their diagnostic and prognostic utility while addressing associated challenges and limitations. Future research directions in this rapidly evolving field are also proposed.

Proteomics profiling of extracellular vesicle for identification of potential biomarkers in Alzheimer's disease: A comprehensive review

The intricate origins and diverse symptoms of Alzheimer's disease (AD) pose significant challenges for both diagnosis and treatment. Exosomes and microvesicles, which carry disease-specific cargo from a variety of central nervous system cell types, have emerged as promising reservoirs of biomarkers for AD. Research on the screening of possible biomarkers in Alzheimer's disease using proteomic profiling of EVs is systematically reviewed in this comprehensive review. We highlight key methodologies employed in EV isolation, characterization, and proteomic analysis, elucidating their advantages and limitations. Furthermore, we summarize the evolving landscape of EV-associated biomarkers implicated in AD pathogenesis, including proteins involved in amyloid-beta metabolism, tau phosphorylation, neuroinflammation, synaptic dysfunction, and neuronal injury. The literature review highlights the necessity for robust validation strategies and standardized protocols to effectively transition EV-based biomarkers into clinical use. In the concluding section, this review delves into potential future avenues and technological advancements pivotal in crafting EV-derived biomarkers applicable to AD diagnostics and prognostics. This review contributes to our comprehension of AD pathology and the advancement of precision medicine in neurodegenerative diseases, hinting at a promising era in AD precision medicine.

Melatonin improves cognitive dysfunction and decreases gliosis in the streptozotocin-induced rat model of sporadic Alzheimer's disease

Introduction

Alzheimer's disease (AD) and other forms of dementia have a devastating effect on the community and healthcare system, as neurodegenerative diseases are causing disability and dependency in older population. Pharmacological treatment options are limited to symptomatic alleviation of cholinergic deficit and accelerated clearance of β-amyloid aggregates, but accessible disease-modifying interventions are needed especially in the early phase of AD. Melatonin was previously demonstrated to improve cognitive function in clinical setting and experimental studies also.

Methods

In this study, the influence of melatonin supplementation was studied on behavioral parameters and morphological aspects of the hippocampus and amygdala of rats. Streptozotocin (STZ) was injected intracerebroventricularly to induce AD-like symptoms in male adult Wistar rats (n = 18) which were compared to age-matched, sham-operated animals (n = 16). Melatonin was administered once daily in a dose of 20 mg/kg body weight by oral route. Behavioral analysis included open-field, novel object recognition, and radial-arm maze tests. TNF-α and MMP-9 levels were determined from blood samples to assess the anti-inflammatory and neuroprotective effects of melatonin. Immunohistological staining of brain sections was performed using anti-NeuN, anti-IBA-1, and anti-GFAP primary antibodies to evaluate the cellular reorganization of hippocampus.

Results and Discussion

The results show that after 40 days of treatment, melatonin improved the cognitive performance of STZ-induced rats and reduced the activation of microglia in both CA1 and CA3 regions of the hippocampus. STZ-injected animals had higher levels of GFAP-labeled astrocytes in the CA1 region, but melatonin treatment reduced this to that of the control group. In conclusion, melatonin may be a potential therapeutic option for treating AD-like cognitive decline and neuroinflammation.

Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

Sociotechnical feasibility of natural language processing-driven tools in clinical trial eligibility prescreening for Alzheimer's disease and related dementias

BACKGROUND

Alzheimer's disease and related dementias (ADRD) affect over 55 million globally. Current clinical trials suffer from low recruitment rates, a challenge potentially addressable via natural language processing (NLP) technologies for researchers to effectively identify eligible clinical trial participants.

OBJECTIVE

This study investigates the sociotechnical feasibility of NLP-driven tools for ADRD research prescreening and analyzes the tools' cognitive complexity's effect on usability to identify cognitive support strategies.

METHODS

A randomized experiment was conducted with 60 clinical research staff using three prescreening tools (Criteria2Query, Informatics for Integrating Biology and the Bedside [i2b2], and Leaf). Cognitive task analysis was employed to analyze the usability of each tool using the Health Information Technology Usability Evaluation Scale. Data analysis involved calculating descriptive statistics, interrater agreement via intraclass correlation coefficient, cognitive complexity, and Generalized Estimating Equations models.

RESULTS

Leaf scored highest for usability followed by Criteria2Query and i2b2. Cognitive complexity was found to be affected by age, computer literacy, and number of criteria, but was not significantly associated with usability.

DISCUSSION

Adopting NLP for ADRD prescreening demands careful task delegation, comprehensive training, precise translation of eligibility criteria, and increased research accessibility. The study highlights the relevance of these factors in enhancing NLP-driven tools' usability and efficacy in clinical research prescreening.

CONCLUSION

User-modifiable NLP-driven prescreening tools were favorably received, with system type, evaluation sequence, and user's computer literacy influencing usability more than cognitive complexity. The study emphasizes NLP's potential in improving recruitment for clinical trials, endorsing a mixed-methods approach for future system evaluation and enhancements.

Numerosity comparison, cognitive strategies, and general cognitive functioning in older people

Introduction

Studies have shown age-related differences in numerical cognition, for example, in the level of numerosity comparison ability. Moreover, some studies point out individual differences in the cognitive strategies employed during the performance of numerosity comparison tasks and reveal that they are related to the aging process. One probable cause of these differences is the level of cognitive functioning. The aim of our study was to determine the relationships among numerosity comparison ability, the cognitive strategies utilized in the performance of numerosity comparison tasks and the general cognitive functioning in older people.

Methods

Forty-seven elderly people participated in the study. The participants were examined using overall cognitive functioning scales and computerized numerosity comparison task.

Results

The results showed many correlations between the participants' level of cognitive functioning and the percent of correct responses (PCR) and response time (RT) during numerosity comparison, as well as with the cognitive strategies applied by the participants. Task correctness was positively related to the level of performance in the attention and executive function tasks. In contrast, the long-term memory resources index and visuospatial skills level were negatively correlated with RT regarding numerosity comparison task performance. The level of long-term memory resources was also positively associated with the frequency of use of more complex cognitive strategies. Series of regression analyses showed that both the level of general cognitive functioning and the cognitive strategies employed by participants in numerosity comparison can explain 9-21 percent of the variance in the obtained results.

Discussion

In summary, these results showed significant relationships between the level of cognitive functioning and proficiency in numerosity comparison measured in older people. Moreover, it has been shown that cognitive resources level is related to the strategies utilized by older people, which indicates the potential application for cognitive strategy examinations in the development of new diagnostic tools.

Lithium and disease modification: A systematic review and meta-analysis in Alzheimer's and Parkinson's disease

The role of lithium as a possible therapeutic strategy for neurodegenerative diseases has generated scientific interest. We systematically reviewed and meta-analyzed pre-clinical and clinical studies that evidenced the neuroprotective effects of lithium in Alzheimer's (AD) and Parkinson's disease (PD). We followed the PRISMA guidelines and performed the systematic literature search using PubMed, EMBASE, Web of Science, and Cochrane Library. A total of 32 articles were identified. Twenty-nine studies were performed in animal models and 3 studies were performed on human samples of AD. A total of 17 preclinical studies were included in the meta-analysis. Our analysis showed that lithium treatment has neuroprotective effects in diseases. Lithium treatment reduced amyloid-β and tau levels and significantly improved cognitive behavior in animal models of AD. Lithium increased the tyrosine hydroxylase levels and improved motor behavior in the PD model. Despite fewer clinical studies on these aspects, we evidenced the positive effects of lithium in AD patients. This study lends further support to the idea of lithium's therapeutic potential in neurodegenerative diseases.

Plasma cell-free RNA profiling of Vietnamese Alzheimer's patients reveals a linkage with chronic inflammation and apoptosis: a pilot study

Introduction

Circulating cell-free RNA (cfRNA) is a potential hallmark for early diagnosis of Alzheimer's Disease (AD) as it construes the genetic expression level, giving insights into the pathological progress from the outset. Profiles of cfRNA in Caucasian AD patients have been investigated thoroughly, yet there was no report exploring cfRNAs in the ASEAN groups. This study examined the gap, expecting to support the development of point-of-care AD diagnosis.

Methods

cfRNA profiles were characterized from 20 Vietnamese plasma samples (10 probable AD and 10 age-matched controls). RNA reads were subjected to differential expression (DE) analysis. Weighted gene correlation network analysis (WGCNA) was performed to identify gene modules that were significantly co-expressed. These modules' expression profiles were then correlated with AD status to identify relevant modules. Genes with the highest intramodular connectivity (module membership) were selected as hub genes. Transcript counts of differentially expressed genes were correlated with key AD measures-MMSE and MTA scores-to identify potential biomarkers.

Results

136 genes were identified as significant AD hallmarks (p < 0.05), with 52 downregulated and 84 upregulated in the AD cohort. 45.6% of these genes are highly expressed in the hippocampus, cerebellum, and cerebral cortex. Notably, all markers related to chronic inflammation were upregulated, and there was a significant shift in all apoptotic markers. Three co-expressed modules were found to be significantly correlated with Alzheimer's status (p < 0.05; R2> 0.5). Functional enrichment analysis on these modules reveals an association with focal adhesion, nucleocytoplasmic transport, and metal ion response leading to apoptosis, suggesting the potential participation of these pathways in AD pathology. 47 significant hub genes were found to be differentially expressed genes with the highest connectivity. Six significant hub genes (CREB1, YTHDC1, IL1RL1, PHACTR2, ANKRD36B, RNF213) were found to be significantly correlated with MTA and MMSE scores. Other significant transcripts (XRN1, UBB, CHP1, THBS1, S100A9) were found to be involved in inflammation and neuronal death. Overall, we have identified candidate transcripts in plasma cf-RNA that are differentially expressed and are implicated in inflammation and apoptosis, which can jumpstart further investigations into applying cf-RNA as an AD biomarker in Vietnam and ASEAN countries.

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

Background

Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.

Objective

This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.

Methods

First, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.

Results

Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.

Conclusion

These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

MicroRNAs and MAPKs: Evidence of These Molecular Interactions in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder known to be the leading cause of dementia worldwide. Many microRNAs (miRNAs) were found deregulated in the brain or blood of AD patients, suggesting a possible key role in different stages of neurodegeneration. In particular, mitogen-activated protein kinases (MAPK) signaling can be impaired by miRNA dysregulation during AD. Indeed, the aberrant MAPK pathway may facilitate the development of amyloid-beta (Aβ) and Tau pathology, oxidative stress, neuroinflammation, and brain cell death. The aim of this review was to describe the molecular interactions between miRNAs and MAPKs during AD pathogenesis by selecting evidence from experimental AD models. Publications ranging from 2010 to 2023 were considered, based on PubMed and Web of Science databases. According to obtained data, several miRNA deregulations may regulate MAPK signaling in different stages of AD and conversely. Moreover, overexpressing or silencing miRNAs involved in MAPK regulation was seen to improve cognitive deficits in AD animal models. In particular, miR-132 is of particular interest due to its neuroprotective functions by inhibiting Aβ and Tau depositions, as well as oxidative stress, through ERK/MAPK1 signaling modulation. However, further investigations are required to confirm and implement these promising results.

Insights into the Pathophysiology of Alzheimer's Disease and Potential Therapeutic Targets: A Current Perspective

The aging population increases steadily because of a healthy lifestyle and medical advancements in healthcare. However, Alzheimer's disease (AD) is becoming more common and problematic among older adults. AD-related cases show an increasing trend annually, and the younger age population may also be at risk of developing this disorder. AD constitutes a primary form of dementia, an irreversible and progressive brain disorder that steadily damages cognitive functions and the ability to perform daily tasks. Later in life, AD leads to death as a result of the degeneration of specific brain areas. Currently, the cause of AD is poorly understood, and there is no safe and effective therapeutic agent to cure or slow down its progression. The condition is entirely preventable, and no study has yet demonstrated encouraging findings in terms of treatment. Identifying this disease's pathophysiology can help researchers develop safe and efficient therapeutic strategies to treat this ailment. This review outlines and discusses the pathophysiology that resulted in the development of AD including amyloid-β plaques, tau neurofibrillary tangles, neuroinflammation, oxidative stress, cholinergic dysfunction, glutamate excitotoxicity, and changes in neurotrophins level may sound better based on the literature search from Scopus, PubMed, ScienceDirect, and Google Scholar. Potential therapeutic strategies are discussed to provide more insights into AD mechanisms by developing some possible pharmacological agents for its treatment.

Early Diagnosis of Alzheimer's Disease with Blood Test; Tempting but Challenging

The increasing prevalence of Alzheimer's disease (AD) has led to a health crisis. According to official statistics, more than 55 million people globally have AD or other types of dementia, making it the sixth leading cause of death. It is still difficult to diagnose AD and there is no definitive diagnosis yet; post-mortem autopsy is still the only definite method. Moreover, clinical manifestations occur very late in the course of disease progression; therefore, profound irreversible changes have already occurred when the disease manifests. Studies have shown that in the preclinical stage of AD, changes in some biomarkers are measurable prior to any neurological damage or other symptoms. Hence, creating a reliable, fast, and affordable method capable of detecting AD in early stage has attracted the most attention. Seeking clinically applicable, inexpensive, less invasive, and much more easily accessible biomarkers for early diagnosis of AD, blood-based biomarkers (BBBs) seem to be an ideal option. This review is an inclusive report of BBBs that have been shown to be altered in the course of AD progression. The aim of this report is to provide comprehensive insight into the research status of early detection of AD based on BBBs.

APOE Allele Frequency in Southern Greece: Exploring the Role of Geographical Gradient in the Greek Population

BACKGROUND

the apolipoprotein e4 allele (APOE4) constitutes an established genetic risk factor for Alzheimer's Disease Dementia (ADD). We aimed to explore the frequency of the APOE isoforms in the Greek population of Southern Greece.

METHODS

peripheral blood from 175 Greek AD patients, 113 with mild cognitive impairment (MCI), and 75 healthy individuals. DNA isolation was performed with a High Pure PCR Template Kit (Roche), followed by amplification with a real-time qPCR kit (TIB MolBiol) in Roche's Light Cycler PCR platform.

RESULTS

APOE4 allele frequency was 20.57% in the ADD group, 17.69% in the MCI group, and 6.67% in the control group. APOE3/3 homozygosity was the most common genotype, while the frequency of APOE4/4 homozygosity was higher in the AD group (8.60%). APOE4 carrier status was associated with higher odds for ADD and MCI (OR: 4.49, 95% CI: [1.90-10.61] and OR: 3.82, 95% CI: [1.59-9.17], respectively).

CONCLUSION

this study examines the APOE isoforms and is the first to report a higher APOE frequency in MCI compared with healthy controls in southern Greece. Importantly, we report the occurrence of the APOE4 allele, related to ADD, as amongst the lowest globally reported, even within the nation, thus enhancing the theory of ethnicity and latitude contribution.

Modulating the Gut Microbiota as a Therapeutic Intervention for Alzheimer's Disease

Growing evidence suggested that the change of composition and proportion of intestinal microbiota may be related to many diseases, such as irritable bowel syndrome, bipolar disorder, Parkinson's disease, as well as Alzheimer's disease. Current literature supports the fact that unbalanced gut microbial composition (gut dysbiosis) is a risk factor for AD. In our review, we briefly sum up the recent progress regarding the correlations between the gut microbiota and AD. Therapeutic interventions capable of modulating the make-up of the gut microflora may exert beneficial effects on AD, preventing or delaying the beginning of AD or counteracting its development. Additionally, well-documented approaches that can positively influence AD may exert their beneficial effects through modifying the gut microbiota. Therefore, other novel interventions which can target on gut microbiota will also be potential therapies for AD. The chances and challenges that AD is confronted with in the research field of microbiomics are also discussed in this review.

Molecular docking and molecular dynamics approach to identify potential compounds in Huperzia squarrosa for treating Alzheimer's disease

OBJECTIVES

Alzheimer's disease (AD) is a lingering progressive neurodegenerative disorder that causes patients to lose cognitive function. The enzyme Acetylcholinesterase (AChE), Butyrylcholinesterase (BuChE), Monoamine oxidase A (MAO A), Beta-secretase cleavage enzyme (BACE 1) and N-methyl-D-aspartate (NMDA) receptors play an important role in the pathogenesis of Alzheimer's disease. Therefore, inhibiting enzymes is an effective method to treat Alzheimer disease. In this study, we evaluated in silico inhibitory effects of AChE, BuChE, MAO A, BACE 1 and NMDA enzyme of Huperzia squarrosa's compounds.

METHODS

The three-dimensional (3D) of N-methyl-D-aspartate receptor (PDB ID: 1PBQ), enzyme β-secretase 1 (PDB ID: 4X7I), enzyme monoamine oxidase A (PDB ID: 2Z5X), enzyme butyrylcholinesterase (PDB ID: 4BDS) and enzyme acetylcholinesterase (PDB ID: 1EVE) were retrieved from the Protein Data Bank RCSB. Molecular docking was done by Autodock vina software and molecular dynamics (MD) simulation of the ligand-protein complex with the least binding energy pose was perfomed by MOE. Lipinski Rule of Five is used to compare compounds with drug-like and non-drug-like properties. Pharmacokinetic parameters of potential compounds were evaluated using the pkCSM tool.

RESULTS

Based on previous publication of Huperzia squarrosa, we have collected 15 compounds. In these compounds, huperzine B, huperzinine, lycoposerramine U N-oxide, 12-epilycodine N-oxide showed strongly inhibit the five AChE, BuChE, MAO A, BACE 1 and NMDA targets for Alzheimer's treatment. Lipinski rule of five and ADMET predict have shown that four above compounds have drug-likeness properties, good absorption ability and cross the blood-brain barrier, which have the most potential to become drugs for the treatment of Alzheimer's in the future. Furthermore, MD study showed that huperzine B and huperzinine have stability of the docking pose with NMDA target.

CONCLUSIONS

In this study, we found two natural compounds in Huperzia squarrosa including Huperzine B and Huperzinine have drug-likeness properties, good absorption ability and cross the blood-brain barrier, which have potential to become drugs for the treatment of Alzheimer's in the future.

Analysis of shared underlying mechanism in neurodegenerative disease

In this review, the relationship between bioenergetics, mitochondrial dysfunction, and inflammation will be and how they contribute to neurodegeneration, specifically in Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS) will be reviewed. Long-term changes in mitochondrial function, autophagy dysfunction, and immune activation are commonalities shared across these age-related disorders. Genetic risk factors for these diseases support an autophagy-immune connection in the underlying pathophysiology. Critical areas of deeper evaluation in these bioenergetic processes may lead to potential therapeutics with efficacy across multiple neurodegenerative diseases.

Deep Learning Techniques for the Effective Prediction of Alzheimer's Disease: A Comprehensive Review

"Alzheimer's disease" (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. "Dementia" is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person's ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. "Deep learning" (DL) is a type of "machine learning" (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered "neural network" architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.

The Helico Maze Detects Early Impairment of Reference Memory at Three Months of Age in the 5XFAD Mouse Model of Alzheimer’s Disease

Background: The 5XFAD model of Alzheimer’s disease (AD) bearing five familial mutations of Alzheimer’s disease on human APP and PSEN1 transgenes shows deposits of amyloid-β peptide (Aβ) as early as 2 months, while deficits in long-term memory can be detected at 4 months using the highly sensitive olfactory-dependent tests that we previously reported. Objective: Given that detecting early dysfunctions in AD prior to overt pathology is of major interest in the field, we sought to detect memory deficits at earlier stages of the disease in 3-month-old male 5XFAD mice. Methods: To this end, we used the Helico Maze, a behavioral task that was recently developed and patented. This device allows deeper analysis of learning and subcategories of hippocampal-dependent long-term memory using olfactory cues. Results: Eight male 5XFAD and 6 male wild-type (WT: C57Bl6 background) mice of 3 months of age were tested in the Helico Maze. The results demonstrated, for the first time, a starting deficit of pure reference long-term memory. Interestingly, memory impairment was clearly correlated with Aβ deposits in the hippocampus. While we also found significant differences in astrogliosis between 5XFAD and WT mice, this was not correlated with memory abilities. Conclusion: Our results underline the efficiency of this new olfactory-dependent behavioral task, which is easy to use, with a small cohort of mice. Using the Helico Maze may open new avenues to validate the efficacy of treatments that target early events related to the amyloid-dependent pathway of the disease and AD progression.

Treatment of dementia: recommendations of the Scientific Department of Cognitive Neurology and Aging of the Brazilian Academy of Neurology

ABSTRACT There is currently no cure for neurodegenerative or vascular dementias, but some pharmacological and non-pharmacological interventions may contribute to alleviate symptoms, slow disease progression and improve quality of life. Current treatment approaches are based on etiology, symptom profile and stage of dementia. This manuscript presents recommendations on pharmacological and non-pharmacological treatments of dementia due to Alzheimer’s disease, vascular cognitive impairment, frontotemporal dementia, Parkinson’s disease dementia, and dementia with Lewy bodies.

Tratamento da demência: recomendações do Departamento Científico de Neurologia Cognitiva e do Envelhecimento da Academia Brasileira de Neurologia

RESUMO Atualmente não há tratamento curativo para as demências neurodegenerativas ou para a demência vascular, mas algumas intervenções farmacológicas e não farmacológicas podem contribuir para aliviar os sintomas, retardar a progressão da doença e melhorar a qualidade de vida. As abordagens terapêuticas atuais são baseadas na etiologia, no perfil dos sintomas e no estágio da demência. Neste artigo apresentamos recomendações sobre os tratamentos farmacológicos e não farmacológicos da demência devida à doença de Alzheimer, comprometimento cognitivo vascular, demência frontotemporal, demência da doença de Parkinson e demência com corpos de Lewy.

Plasma Lipidomics Approach in Early and Specific Alzheimer’s Disease Diagnosis

Background: The brain is rich in lipid content, so a physiopathological pathway in Alzheimer’s disease (AD) could be related to lipid metabolism impairment. The study of lipid profiles in plasma samples could help in the identification of early AD changes and new potential biomarkers. Methods: An untargeted lipidomic analysis was carried out in plasma samples from preclinical AD (n = 11), mild cognitive impairment-AD (MCI-AD) (n = 31), and healthy (n = 20) participants. Variables were identified by means of two complementary methods, and lipid families’ profiles were studied. Then, a targeted analysis was carried out for some identified lipids. Results: Statistically significant differences were obtained for the diglycerol (DG), lysophosphatidylethanolamine (LPE), lysophosphatidylcholine (LPC), monoglyceride (MG), and sphingomyelin (SM) families as well as for monounsaturated (MUFAs) lipids, among the participant groups. In addition, statistically significant differences in the levels of lipid families (ceramides (Cer), LPE, LPC, MG, and SM) were observed between the preclinical AD and healthy groups, while statistically significant differences in the levels of DG, MG, and PE were observed between the MCI-AD and healthy groups. In addition, 18:1 LPE showed statistically significant differences in the targeted analysis between early AD (preclinical and MCI) and healthy participants. Conclusion: The different plasma lipid profiles could be useful in the early and minimally invasive detection of AD. Among the lipid families, relevant results were obtained from DGs, LPEs, LPCs, MGs, and SMs. Specifically, MGs could be potentially useful in AD detection; while LPEs, LPCs, and SM seem to be more related to the preclinical stage, while DGs are more related to the MCI stage. Specifically, 18:1 LPE showed a potential utility as an AD biomarker.

Molecular Insights into Therapeutic Potentials of Hybrid Compounds Targeting Alzheimer's Disease

Alzheimer's disease (AD) is one of the most complex progressive neurological disorders involving degeneration of neuronal connections in brain cells leading to cell death. AD is predominantly detected among elder people (> 65 years), mostly diagnosed with the symptoms of memory loss and cognitive dysfunctions. The multifarious pathogenesis of AD comprises the accumulation of pathogenic proteins, decreased neurotransmission, oxidative stress, and neuroinflammation. The conventional therapeutic approaches are limited to symptomatic benefits and are ineffective against disease progression. In recent years, researchers have shown immense interest in the designing and fabrication of various novel therapeutics comprised of naturally isolated hybrid molecules. Hybrid therapeutic compounds are developed from the combination of pharmacophores isolated from bioactive moieties which specifically target and block various AD-associated pathogenic pathways. The method of designing hybrid molecules has numerous advantages over conventional multitarget drug development methods. In comparison to in silico high throughput screening, hybrid molecules generate quicker results and are also less expensive than fragment-based drug development. Designing hybrid-multitargeted therapeutic compounds is thus a prospective approach in developing an effective treatment for AD. Nevertheless, several issues must be addressed, and additional researches should be conducted to develop hybrid therapeutic compounds for clinical usage while keeping other off-target adverse effects in mind. In this review, we have summarized the recent progress on synthesis of hybrid compounds, their molecular mechanism, and therapeutic potential in AD. Using synoptic tables, figures, and schemes, the review presents therapeutic promise and potential for the development of many disease-modifying hybrids into next-generation medicines for AD.

Differential diagnosis of vascular cognitive impairment

Background. Vascular cognitive impairment (VCI), аccording to world statistics, is the second most common cause of cognitive impairment after Alzheimer's disease (AD). Differential diagnosis between the two most common nosological forms of cognitive impairment is not always simple and is based on the clinical characteristic, neuropsychological and neuroradiological features of the disease. Aim. To present a differential diagnosis between VCI and AD using the example of a clinical case of vascular mild cognitive impairment. Materials and methods. A 68-year-old female patient with mild cognitive impairment underwent a neuropsychological testing: the Mini-mental state exam, Frontal assessment battery, the MoCA-test, the Clock-Drawing Test, the 12 Words test, Verbal Association Technique (literal and categorical), The Trail making test (A and B), The digit symbol substitution test, The Boston naming test (40 images), EXIT-25, Beck Depression Scale, Hospital anxiety and depression scale; neuroimaging magnetic resonance imaging in T1, T2, FLAIR, DWI, SWI sequence. Results. On the basis of clinical data, the results of neuropsychological testing and neuroimaging, the differential diagnosis with AD, the patient was diagnosed with vascular mild cognitive impairment. Nootropic therapy (choline alfoscerate) was prescribed and recommendations on risk factors correction were given. Conclusion. The article describes a patient with mild VCI. This case study discusses the main differences between AD and VCI that can be used as a basis for differential diagnosis. The features of cognitive impairments in VCI and AD are described, including the predominance of attention impairment, an executive function disorder in VCI and an impairment of memory progressing according to the Ribots law in AD. Differences in visuospatial skills and language in VCI and AD are discussed. The most reliable neuroradiological signs of cerebrovascular diseases are described.

Neuropsychiatric symptoms and cerebrovascular risk in non-demented elders: cross-sectional study using the mild behavioural impairment checklist (MBI-C)

BACKGROUND

Neuropsychiatric symptoms (NPS) may represent early clinical manifestations of evolving brain diseases. Studies underpin the occurrence of NPS in the context of mild cognitive impairment (MCI) and prodromal Alzheimer's disease, where symptoms referred to as 'mild behavioural impairment' (MBI) have been shown to predict conversion to dementia and to hasten cognitive/functional decline. However, the association between NPS and cerebrovascular risk factors has been poorly investigated, despite the high prevalence of the latter among individuals with MCI. The aim of the present study was to investigate the association between MBI and cerebrovascular risk in a clinical sample of non-demented elders.

METHODS

Sixty-five MCI and 15 cognitively unimpaired older adults were cross-sectionally assessed with the Mild Behavioural Impairment Checklist (MBI-C), using the cut-off score > 6.5 to define positive screening. Participants were submitted to the Hachinski Ischaemic Score (HIS) to account for cerebrovascular symptoms, vascular risk, and related comorbidities. Neuroimaging scans (magnetic resonance imaging and/or 18F-fluorodeoxyglucose-positron emission tomography) and apolipoprotein E genotype were obtained.

RESULTS

Positive associations were found between total MBI-C scores and increasing number of comorbidities present (0-2 comorbidities), but not with three comorbidities. Two domains of the MBI-C-impulse dyscontrol and social inappropriateness-followed the same trend of the MBI-C total score, with higher scores with the increasing numbers of comorbidities. No significant associations were found between MBI symptoms and HIS or cerebrovascular burden in neuroimaging assessment.

CONCLUSION

We found weak associations between MBI-C total score and the presence of comorbidities with cerebrovascular risk, but not with structural or functional neuroimaging abnormalities or HIS. This finding may represent that the presence of comorbidities adds limited risk to the occurrence of MBI in this sample of non-demented elders.

Doživljanje umiranja z vidika medicinskih sester

Uvod: V enotah intenzivne terapije se medicinske sestrednevno soočajo z umiranjem. To jim predstavlja velike obremenitve, ki vodijo do stiske. Namen pregleda literature je ugotoviti, kaj doživljajo medicinske sestre ob umirajočih pacientih v enotah intenzivne terapije in kako se soočajo z umiranjem.Metode: Uporabljena je metoda pregleda literature. Iskanje je bilo izvedeno v bazah podatkov: PubMed, ScienceDirect, SAGE, Web of Science in CINAHL na osnovi vključitvenih in izključitvenih kriterijev. Proces iskanja literature je prikazan z diagramom PRISMA. Za analizo in sintezo podatkov je bila uporabljena metoda tematske analize.Rezultati: Od 1.886 zadetkov smo v končno analizo vključili 10 člankov. Na podlagi tematske analize smo oblikovali dve glavni temi: (1) doživljanje medicinskih sester ob hudo bolnih in umirajočih pacientih; (2) profesionalni odnos medicinskih sester.Diskusija in zaključek: Izvajanje zdravstvene nege v enotah intenzivne terapije je za medicinske sestre zelo stresno. Pri zdravstveni negi kritično bolnih pacientov se najpogosteje srečujejo s stalnimi fizičnimi obremenitvami, čustvenimi pretresi, pomanjkanjem čustvene podpore, žalostjo, izgorevanjem, depresijo, težavami v komunikaciji s pacientovimi sorodniki ter pomanjkljivim sodelovanjem in zagotavljanjem podpore v timu. Izpostavljene so številnim dejavnikom, ki prispevajo k doživljanju lastne stiske. Potrebni so ukrepi za zaščito, učinkovitejšo pomoč in podporo medicinskim sestram, ki se dnevno soočajo z umiranjem in zagotavljanjem podpore pacientovi družini oziroma svojcem. 

The Progress of Label-Free Optical Imaging in Alzheimer's Disease Screening and Diagnosis

As the major neurodegenerative disease of dementia, Alzheimer's disease (AD) has caused an enormous social and economic burden on society. Currently, AD has neither clear pathogenesis nor effective treatments. Positron emission tomography (PET) and magnetic resonance imaging (MRI) have been verified as potential tools for diagnosing and monitoring Alzheimer's disease. However, the high costs, low spatial resolution, and long acquisition time limit their broad clinical utilization. The gold standard of AD diagnosis routinely used in research is imaging AD biomarkers with dyes or other reagents, which are unsuitable for in vivo studies owing to their potential toxicity and prolonged and costly process of the U.S. Food and Drug Administration (FDA) approval for human use. Furthermore, these exogenous reagents might bring unwarranted interference to mechanistic studies, causing unreliable results. Several label-free optical imaging techniques, such as infrared spectroscopic imaging (IRSI), Raman spectroscopic imaging (RSI), optical coherence tomography (OCT), autofluorescence imaging (AFI), optical harmonic generation imaging (OHGI), etc., have been developed to circumvent this issue and made it possible to offer an accurate and detailed analysis of AD biomarkers. In this review, we present the emerging label-free optical imaging techniques and their applications in AD, along with their potential and challenges in AD diagnosis.

Heterogeneity of Cerebrospinal Fluid Biomarkers Profiles in Individuals with Distinct Levels of Cognitive Decline: A Cross-Sectional Study

BACKGROUND

Decreased cerebrospinal fluid (CSF) concentrations of the amyloid-β (Aβ), along with increased total (T-tau) and phosphorylated tau protein (P-tau), are widely accepted as core biomarkers of Alzheimer's disease (AD) pathology. Nonetheless, there are a few remaining caveats that still preclude the full incorporation of AD biomarkers into clinical practice.

OBJECTIVE

To determine the frequency of clinical-biological mismatches in a clinical sample of older adults with varying degrees of cognitive impairment.

METHODS

204 participants were enrolled for a cross-sectional assessment and allocated into diagnostic groups: probable AD (n = 60, 29.4%); MCI (n = 84, 41.2%); or normal cognition (NC, n = 60, 29.4%). CSF concentrations of Aβ42, T-tau, and 181Thr-P-tau were determined, and Aβ42/P-tau ratio below 9.53 was used as a proxy of AD pathology. The AT(N) classification was further used as a framework to ascertain the biological evidence of AD.

RESULTS

The majority (73.7%) of patients in the AD group had the Aβ42/P-tau ratio below the cut-off score for AD, as opposed to a smaller proportion in the MCI (42.9%) and NC (23.3%) groups. In the latter, 21 subjects (35%) were classified as A+, 28 (46.7%) as T+, and 23 (38.3%) as N + . In the AD group, 66.7%of the cases were classified as A+, 78.3%as T+, and 80%as N+.

CONCLUSION

Analysis of CSF biomarkers was able to discriminate between AD, MCI, and NC. However, clinical-biological mismatches were observed in a non-negligible proportion of cases.

The Helico Maze allows testing of early learning and subcategories of long-term memory in mice

Different memory systems operate in parallel to support behaviour. To evaluate procedural and reference subcategories of long-term memory as early as possible in the mouse, the Helico Maze (HM) was developed. BALB/c AnNCrl (BALB), C57BL/6JRj (C57) and DBA/2 JRj (DBA) mice were trained on this new maze. The three strains learned how to use the HM (procedural memory), and they then learned and remembered four odour-reward associations (reference memory). The three strains differed in the number of correct responses. BALB mice showed better performance than C57 and DBA mice. The results of the first block of each session revealed that only the BALB and C57 mice remembered the odour-reward associations. DBA mice needed to relearn the associations each day. With this new apparatus, the number of olfactory cue-reward associations was increased from 2 to 4 in comparison to a previous olfactory tubing maze. Consequently, a supplementary effort of memory was required, and the chance level was decreased from 50 % to 25 %. Thus, in several important respects, the HM can be considered to measure the hippocampus-dependent behaviour of the mouse, allowing to study, as early as possible in young mice, the different subcategories of long-term memory, such as those observed in humans.

Dissecting Sex-Related Cognition between Alzheimer's Disease and Diabetes: From Molecular Mechanisms to Potential Therapeutic Strategies

The brain is a sexually dimorphic organ that implies different functions and structures depending on sex. Current pharmacological approaches against different neurological diseases act distinctly in male and female brains. In all neurodegenerative diseases, including Alzheimer's disease (AD), sex-related outcomes regarding pathogenesis, prevalence, and response to treatments indicate that sex differences are important for precise diagnosis and therapeutic strategy. Pathogenesis of AD includes vascular dementia, and in most cases, this is accompanied by metabolic complications with similar features as those assembled in diabetes. This review discusses how AD-associated dementia and diabetes affect cognition in relation to sex difference, as both diseases share similar pathological mechanisms. We highlight potential protective strategies to mitigate amyloid-beta (Aβ) pathogenesis, emphasizing how these drugs act in the male and female brains.

A preliminary study about neurofilament light chain and tau protein levels in psoriasis: Correlation with disease severity

BACKGROUND

Studies investigating cognitive dysfunction in psoriatic patients remain inconclusive.

OBJECTIVE

To investigate the risk of cognitive decline in plaque-type psoriasis patients.

METHODS

Serum neurofilament light chain (NFL) and tau protein concentrations in 45 patients with plaque-type psoriasis and forty-five healthy controls were measured by enzyme-linked immunosorbent assay (ELISA).

RESULTS

Mean homeostasis model assessment (HOMA-IR) values (6.82 vs 3.25) and serum levels of insulin (28.19 vs 15.71), NFL (5.74 vs 1.98), and tau (348.17 vs 207.30) in patients with psoriasis were found to be significantly higher than those of in healthy controls. There was a significant positive correlation between NFL and tau (r = .257, P = .015). There was significant correlation between NFL, tau and PASI (r = .310, P = .040) and (r = .383, P = .010), respectively. Significant correlations between NFL and insulin, TC, HDL-C, TG, VLDL-C, and BMI were found. NFL (9.38 vs 3.08) and tau (439.28 vs 281.58) concentrations and PASI values (23.94 vs 14.18) in patients with disease onset before 40 years were significantly higher than that of the patients with disease onset after 40 years. C-reactive protein (CRP) was significantly correlated with BMI (r = .449, P < .001), LDL-C (r = .240, P = .026), TG (r = .244, P = .024), and VLDL-C (r = .241, P = .025) in patients with psoriasis.

CONCLUSIONS

Increased serum NFL and tau protein levels and the presence of positive correlations between NFL, tau protein and PASI score show cognitive decline risk may be higher in moderate-to-severe psoriasis.

Role of Body-Fluid Biomarkers in Alzheimer's Disease Diagnosis

Alzheimer’s disease (AD) is a complex neurodegenerative disease that requires extremely specific biomarkers for its diagnosis. For current diagnostics capable of identifying AD, the development and validation of early stage biomarkers is a top research priority. Body-fluid biomarkers might closely reflect synaptic dysfunction in the brain and, thereby, could contribute to improving diagnostic accuracy and monitoring disease progression, and serve as markers for assessing the response to disease-modifying therapies at early onset. Here, we highlight current advances in the research on the capabilities of body-fluid biomarkers and their role in AD pathology. Then, we describe and discuss current applications of the potential biomarkers in clinical diagnostics in AD.